Research Article
REST-MapReduce: An Integrated Interface but
Differentiated Service
Jong-Hyuk Park,^1 Hwa-Young Jeong,^2 Young-Sik Jeong,^3 and Min Choi^4
(^1) Department of Computer Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro,
Nowon-gu, Seoul 139-743, Republic of Korea
(^2) Humanitas College, Kyunghee University, No. 26, Kyunghee-daero, Dongdaemun-gu, Seoul 130-701, Republic of Korea
(^3) Department of Multimedia Engineering, Dongguk University, 30 Pildong-ro 1 Gil, Jung-gu, Seoul 100-715, Republic of Korea
(^4) Department of Information and Communication Engineering, Chungbuk National University, 52 Naesudong-ro,
Heungdeok-gu, Chungbuk, Cheongju 361-763, Republic of Korea
Correspondence should be addressed to Min Choi; [email protected]
Received 16 March 2014; Accepted 3 April 2014; Published 11 June 2014
Academic Editor: Laurence T. Yang
Copyright © 2014 Jong-Hyuk Park et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the fast deployment of cloud computing, MapReduce architectures are becoming the major technologies for mobile cloud
computing. The concept of MapReduce was first introduced as a novel programming model and implementation for a large set
of computing devices. In this research, we propose a novel concept of REST-MapReduce, enabling users to use only the REST
interface without using the MapReduce architecture. This approach provides a higher level of abstraction by integration of the
two types of access interface, REST API and MapReduce. The motivation of this research stems from the slower response time for
accessing simple RDBMS on Hadoop than direct access to RDMBS. This is because there is overhead to job scheduling, initiating,
starting, tracking, and management during MapReduce-based parallel execution. Therefore, we provide a good performance for
REST Open API service and for MapReduce, respectively. This is very useful for constructing REST Open API services on Hadoop
hosting services, for example, Amazon AWS (Macdonald, 2005) or IBM Smart Cloud. For evaluating performance of our REST-
MapReduce framework, we conducted experiments with Jersey REST web server and Hadoop. Experimental result shows that our
approach outperforms conventional approaches.
1. Introduction
With the fast deployment of cloud computing, MapReduce
architectures are becoming the major technologies for mobile
cloud computing. Nowadays, we are experiencing a major
shift from conventional mobile applications to mobile cloud
computing. The demand of Open API-based development
stems from the increasing use of smartphone applications
[ 1 , 2 ]. Community portal companies are providing Open API
service for access to their service. Within a few years, we
can expect a major shift from traditional mobile application
technology to mobile cloud computing [ 3 ]. It improves appli-
cation performance and efficiency by off-loading complex
and time-consuming tasks onto powerful computing plat-
forms. By running only simple tasks on mobile devices, we
can achieve a longer battery lifetime and a greater processing
efficiency. This off-loading with the use of parallelism is not
only faster but can also be used to solve problems related to
large data sets of nonlocal resources. With a set of computers
connectedonanetwork,thereisavastpoolofCPUsand
resources, and you have the ability to access files on a cloud.
Inthispaper,weproposeanovelapproachthatrealizesthe
mobile cloud convergence in a transparent and platform-
independent way. Users need not know how their jobs are
actually executed in a distributed environment and need not
to take into account their mobile platforms are IPhone or
Android.AlltheyhavetodoistomakeuseoftheREST
interface, and need not to know the complex distributed
computing API such as Hadoop [ 4 ].
The research of MapReduce using REST web service
interface is underexplored and most research efforts are still
at their initial state [ 5 , 6 ]. MapReduce is a programming
model and an associated implementation for processing and
generatinglargedatasets.Inthiswork,weproposeaconceptHindawi Publishing Corporation
Journal of Applied Mathematics
Volume 2014, Article ID 170723, 10 pages
http://dx.doi.org/10.1155/2014/170723